mirror of
https://github.com/CopilotKit/CopilotKit.git
synced 2026-09-14 16:26:20 +08:00
Alem Tuzlak
c91a9c9aff
Two follow-up fixes layered on the previous internal-tool rename: (1) `a2ui_fixed.py` — fixed-schema demo infinite loop on deploy. The `display_flight` tool returns the raw `a2ui.render(...)` JSON descriptor as its tool result. gpt-4o-mini reads that opaque blob, can't tell the flight was rendered, and re-calls `display_flight` indefinitely (visible on the deployed showcase as 6+ duplicate flight cards stacked under repeated assistant text). Local was just lucky. Hardened the docstring + system prompt to spell out: the JSON return value is the surface descriptor, the card is already rendered, do NOT call again, reply with one short confirmation and stop. (2) Rename `render_a2ui` → `_design_a2ui_surface` in shared and langgraph-python parity copies of `tools/generate_a2ui.py` (+ `tools/__init__.py` re-export `RENDER_A2UI_TOOL_SCHEMA` → `DESIGN_A2UI_SURFACE_TOOL_SCHEMA`), and in `showcase/shared/typescript/ tools/generate-a2ui.ts`. These shared helpers were the source-of-truth for the secondary-LLM tool name across integrations; renaming here keeps parity with the langgraph-python agents already renamed in `beautiful_chat.py` / `a2ui_dynamic.py`. Other framework integrations keep their own `render_a2ui` for now (separate parity sweep). (3) `showcase/aimock/feature-parity.json` — added a sibling fixture matching `toolName: "_design_a2ui_surface"` for the beautiful-chat Sales Dashboard pill so the langgraph-python e2e suite still hits a deterministic mock on Railway. The original `render_a2ui` fixture is kept above it so other integrations whose secondary LLM still requests `render_a2ui` continue to match. (4) Comment update in `beautiful-chat.spec.ts` to name the new internal tool.
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
"""Dynamic A2UI tool: LLM-generated UI from conversation context.
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This module provides the data preparation for a secondary LLM call that
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generates v0.9 A2UI components. The actual LLM call is made by the
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framework-specific wrapper (LangGraph, CrewAI, etc.) since each framework
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has its own way of invoking LLMs.
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Optional
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_logger = logging.getLogger(__name__)
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CUSTOM_CATALOG_ID = "copilotkit://app-dashboard-catalog"
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# The _design_a2ui_surface tool schema that the secondary LLM is bound to.
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DESIGN_A2UI_SURFACE_TOOL_SCHEMA = {
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"name": "_design_a2ui_surface",
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"description": (
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"Render a dynamic A2UI v0.9 surface.\n\n"
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"Args:\n"
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" surfaceId: Unique surface identifier.\n"
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" catalogId: The catalog ID (use \"copilotkit://app-dashboard-catalog\").\n"
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" components: A2UI v0.9 component array (flat format). "
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"The root component must have id \"root\".\n"
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" data: Optional initial data model for the surface."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"surfaceId": {"type": "string", "description": "Unique surface identifier."},
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"catalogId": {"type": "string", "description": "The catalog ID."},
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"components": {
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"type": "array",
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"items": {"type": "object"},
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"description": "A2UI v0.9 component array (flat format).",
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},
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"data": {
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"type": "object",
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"description": "Optional initial data model for the surface.",
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},
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},
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"required": ["surfaceId", "catalogId", "components"],
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},
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}
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def generate_a2ui_impl(
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messages: list[dict[str, Any]],
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context_entries: Optional[list[dict[str, Any]]] = None,
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) -> dict[str, Any]:
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"""Prepare inputs for a secondary LLM call that generates A2UI components.
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Returns a dict with:
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- system_prompt: The system prompt for the secondary LLM (built from context)
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- tool_schema: The _design_a2ui_surface tool schema to bind to the LLM
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- tool_choice: The tool name to force
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- messages: The conversation messages to pass through
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- catalog_id: The default catalog ID
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The framework wrapper should:
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1. Make an LLM call with these inputs
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2. Extract the tool call args (surfaceId, catalogId, components, data)
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3. Build a2ui_operations from the args and return them
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"""
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context_text = ""
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if context_entries:
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context_text = "\n\n".join(
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entry.get("value", "")
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for entry in context_entries
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if isinstance(entry, dict) and entry.get("value")
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)
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return {
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"system_prompt": context_text,
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"tool_schema": DESIGN_A2UI_SURFACE_TOOL_SCHEMA,
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"tool_choice": "_design_a2ui_surface",
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"messages": messages,
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"catalog_id": CUSTOM_CATALOG_ID,
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}
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def build_a2ui_operations_from_tool_call(args: dict[str, Any]) -> dict[str, Any]:
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"""Build a2ui_operations dict from the secondary LLM's tool call args.
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Call this after the framework wrapper extracts the tool call arguments.
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"""
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surface_id = args.get("surfaceId", "dynamic-surface")
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catalog_id = args.get("catalogId", CUSTOM_CATALOG_ID)
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components = args.get("components", [])
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if not components:
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_logger.warning("build_a2ui_operations_from_tool_call received empty components list")
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data = args.get("data")
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ops = [
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{"type": "create_surface", "surfaceId": surface_id, "catalogId": catalog_id},
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{"type": "update_components", "surfaceId": surface_id, "components": components},
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]
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if data:
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ops.append({"type": "update_data_model", "surfaceId": surface_id, "data": data})
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return {"a2ui_operations": ops}
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